EVIT: Event-Oriented Instruction Tuning for Event Reasoning

📄 arXiv: 2404.11978v1 📥 PDF

作者: Zhengwei Tao, Xiancai Chen, Zhi Jin, Xiaoying Bai, Haiyan Zhao, Yiwei Lou

分类: cs.CL

发布日期: 2024-04-18

💡 一句话要点

提出事件导向指令调优方法以提升事件推理能力

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 事件推理 指令调优 事件四元组 自然语言处理 模型微调

📋 核心要点

  1. 现有的小型指令调优模型在事件推理任务中表现不佳，主要由于缺乏对事件结构和语义的明确建模。
  2. 本文提出事件导向指令调优（EvIT），通过引入事件四元组结构和事件关系学习来提升模型的事件推理能力。
  3. 实验结果显示，EvIT在多个数据集上的事件推理任务中表现优异，达到了竞争力的性能水平。

📝 摘要（中文）

事件是指在特定背景下发生的特定事件或事故。事件推理旨在根据特定关系推断事件并预测未来事件。尽管大型语言模型在事件推理方面取得了显著进展，但当前较小的指令调优模型在处理这些任务时表现不佳，主要由于缺乏对事件及其相互关系的明确建模。为此，本文提出了事件导向指令调优（EvIT），通过引入事件四元组结构来增强模型的事件推理能力，并在多个数据集上进行了广泛实验，结果表明EvIT在事件推理任务上表现出色。

🔬 方法详解

问题定义：本文旨在解决现有小型指令调优模型在事件推理任务中的不足，主要体现在对事件结构和语义的理解不够深入，导致推理能力受限。

核心思路：提出事件导向指令调优（EvIT），通过设计事件四元组来完整表示事件的结构和语义，并结合事件关系学习来增强模型的推理能力。

技术框架：整体流程包括事件四元组的挖掘、事件关系学习和指令调优三个主要模块。首先，从大规模语料库中挖掘事件四元组，然后进行事件关系学习，最后将学习结果应用于指令调优。

关键创新：最重要的创新在于引入事件四元组这一新结构，使得模型能够更好地理解事件之间的关系，从而提升事件推理的准确性和有效性。

关键设计：在模型训练中，采用无监督的方法挖掘事件四元组，并设计特定的损失函数以优化事件关系学习的效果，同时对Llama模型进行微调以适应事件导向的任务。

📊 实验亮点

实验结果表明，EvIT在多个事件推理任务上表现出色，相较于基线模型，性能提升显著，自动评估和人工评估均显示出其在事件推理能力上的竞争力。

🎯 应用场景

该研究的潜在应用领域包括自然语言处理中的事件推理、信息检索和对话系统等。通过提升模型对事件的理解和推理能力，可以在多个实际场景中实现更智能的交互和决策支持，未来可能对智能助手和自动化系统产生深远影响。

📄 摘要（原文）

Events refer to specific occurrences, incidents, or happenings that take place under a particular background. Event reasoning aims to infer events according to certain relations and predict future events. The cutting-edge techniques for event reasoning play a crucial role in various natural language processing applications. Large language models (LLMs) have made significant advancements in event reasoning owing to their wealth of knowledge and reasoning capabilities. However, smaller instruction-tuned models currently in use do not consistently demonstrate exceptional proficiency in managing these tasks. This discrepancy arises from the absence of explicit modeling of events and the interconnections of them within their instruction data. Consequently, these models face challenges in comprehending event structures and semantics while struggling to bridge the gap between their interpretations and human understanding of events. Additionally, their limitations in grasping event relations lead to constrained event reasoning abilities to effectively deduce and incorporate pertinent event knowledge. In this paper, we propose Event-Oriented Instruction Tuning (EvIT) to train our LLM. Specifically, we first propose a novel structure named event quadruple which contains the structure and semantics of events and is complete in the event representation. We then design event-relation learning based on the structures. We encapsulate the learning into the instruction-tuning formulation to better stimulate the event reasoning capacity of our model. We design a heuristic unsupervised method to mine event quadruple from a large-scale corpus. At last, we finetune a Llama model on our Event-Oriented Instruction Tuning. We conduct extensive experiments on event reasoning tasks on several datasets. Automatic and human evaluations demonstrate EvIT achieves competitive performances on event reasoning.